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It is thus suggested to use a single implicit direction (for efficient computing), or the same implicit factor in each direction.
We demonstrate how the coarseness of modeling grid in comparison to source (comp'utational receivers) spacing can be exploited for efficient computing, without compromising the quality of the inverted model, by reducing the number of adjoint calls.
The main contribution of this paper consists in the development of a new alternative method for efficient computing of a composite length DFT, when the input sequence and/or the required output sequence are smaller than the length of the full transform.
Our method is easy to operate, requiring only two parameters: noise standard deviation, σ, as an input for BM3D denoising and the step interval, s, between two neighboring denoising operations in the iteration (denoising is not necessary for each iteration step for efficient computing).
For the treatment of unconfined systems, spatial discretization was chosen coarser for efficient computing but never exceeded 0.1 µm.
The sparse structure of the matrix in equation (1) is the corner-stone for efficient computing using a single-step method.
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The contractions were then resampled at 20 Hz for more efficient computing.
This increase in data volume consequently increases the need for an efficient computing framework to process this data and transform it into meaningful information.
The other is that the discrete-time models can also govern efficient computing models for continuous ones in order to achieve the numerical emulation.
The back-propagation algorithm has two main advantages: local for updating the synaptic weights and biases, and efficient for computing all the partial derivatives of the cost function with respect to these free parameters.
This method is efficient for computing compressible flows in slow unsteady regimes, but for quick unsteady flows, it may be costly and not accurate enough.
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